mirror of https://github.com/coqui-ai/TTS.git
125 lines
4.0 KiB
Python
125 lines
4.0 KiB
Python
import os
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import librosa
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import pickle
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import numpy as np
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from scipy import signal
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_mel_basis = None
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class AudioProcessor(object):
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def __init__(self, sample_rate, num_mels, min_level_db, frame_shift_ms,
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frame_length_ms, preemphasis, ref_level_db, num_freq, power,
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griffin_lim_iters=None):
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self.sample_rate = sample_rate
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self.num_mels = num_mels
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self.min_level_db = min_level_db
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self.frame_shift_ms = frame_shift_ms
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self.frame_length_ms = frame_length_ms
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self.preemphasis = preemphasis
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self.ref_level_db = ref_level_db
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self.num_freq = num_freq
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self.power = power
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self.griffin_lim_iters = griffin_lim_iters
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def save_wav(self, wav, path):
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wav *= 32767 / max(0.01, np.max(np.abs(wav)))
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librosa.output.write_wav(path, wav.astype(np.int16), self.sample_rate)
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def _linear_to_mel(self, spectrogram):
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global _mel_basis
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if _mel_basis is None:
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_mel_basis = self._build_mel_basis()
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return np.dot(_mel_basis, spectrogram)
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def _build_mel_basis(self, ):
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n_fft = (self.num_freq - 1) * 2
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return librosa.filters.mel(self.sample_rate, n_fft, n_mels=self.num_mels)
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def _normalize(self, S):
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return np.clip((S - self.min_level_db) / -self.min_level_db, 0, 1)
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def _denormalize(self, S):
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return (np.clip(S, 0, 1) * -self.min_level_db) + self.min_level_db
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def _stft_parameters(self, ):
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n_fft = (self.num_freq - 1) * 2
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hop_length = int(self.frame_shift_ms / 1000 * self.sample_rate)
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win_length = int(self.frame_length_ms / 1000 * self.sample_rate)
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return n_fft, hop_length, win_length
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def _amp_to_db(self, x):
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return 20 * np.log10(np.maximum(1e-5, x))
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def _db_to_amp(self, x):
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return np.power(10.0, x * 0.05)
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def apply_preemphasis(self, x):
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return signal.lfilter([1, -self.preemphasis], [1], x)
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def apply_inv_preemphasis(self, x):
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return signal.lfilter([1], [1, -self.preemphasis], x)
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def spectrogram(self, y):
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D = self._stft(self.apply_preemphasis(y))
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S = self._amp_to_db(np.abs(D)) - self.ref_level_db
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return self._normalize(S)
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def inv_spectrogram(self, spectrogram):
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'''Converts spectrogram to waveform using librosa'''
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S = self._denormalize(spectrogram)
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S = self._db_to_amp(S + self.ref_level_db) # Convert back to linear
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# Reconstruct phase
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return self.apply_inv_preemphasis(self._griffin_lim(S ** self.power))
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def _griffin_lim(self, S):
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'''librosa implementation of Griffin-Lim
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Based on https://github.com/librosa/librosa/issues/434
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'''
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angles = np.exp(2j * np.pi * np.random.rand(*S.shape))
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S_complex = np.abs(S).astype(np.complex)
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y = self._istft(S_complex * angles)
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for i in range(self.griffin_lim_iters):
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angles = np.exp(1j * np.angle(self._stft(y)))
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y = self._istft(S_complex * angles)
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return y
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def melspectrogram(self, y):
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D = self._stft(self.apply_preemphasis(y))
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S = self._amp_to_db(self._linear_to_mel(np.abs(D))) - self.ref_level_db
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return self._normalize(S)
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def _stft(self, y):
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n_fft, hop_length, win_length = self._stft_parameters()
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return librosa.stft(y=y, n_fft=n_fft, hop_length=hop_length, win_length=win_length)
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def _istft(self, y):
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_, hop_length, win_length = self._stft_parameters()
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return librosa.istft(y, hop_length=hop_length, win_length=win_length)
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def find_endpoint(self, wav, threshold_db=-40, min_silence_sec=0.8):
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window_length = int(self.sample_rate * min_silence_sec)
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hop_length = int(window_length / 4)
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threshold = self._db_to_amp(threshold_db)
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for x in range(hop_length, len(wav) - window_length, hop_length):
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if np.max(wav[x:x + window_length]) < threshold:
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return x + hop_length
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return len(wav)
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